Modeling and control of drug delivery systems using adaptive neural control methods

Author

Polycarpou, Marios M. ; Conway, John Y.

Author_Institution

Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA

Volume

1

fYear

1995

fDate

21-23 Jun 1995

Firstpage

781

Abstract

This paper investigates the use of adaptive neural network techniques for modeling and control of mean arterial pressure through the intravenous infusion of sodium nitroprusside. A model reference based adaptive nonlinear control scheme with neural networks replacing the unknown nonlinearities is developed. In this formulation nonlinear estimators are used to augment the linear control law for improved performance. Computer simulations illustrate the ability of radial basis function (RBF) networks to model the unknown nonlinearities and improve the closed-loop system characteristics

Keywords

biomedical equipment; haemodynamics; iron compounds; model reference adaptive control systems; neurocontrollers; nonlinear control systems; patient treatment; pressure control; sodium compounds; Na₂Fe(CN)₅NO; adaptive neural control methods; closed-loop system characteristics; drug delivery systems; intravenous infusion; mean arterial pressure control; model reference based adaptive nonlinear control scheme; neural network; radial basis function networks; sodium nitroprusside; Adaptive control; Adaptive systems; Automatic control; Biomedical monitoring; Blood pressure; Drug delivery; Neural networks; Pressure control; Programmable control; Surgery;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, Proceedings of the 1995

Conference_Location

Seattle, WA

Print_ISBN

0-7803-2445-5

Type

conf

DOI

10.1109/ACC.1995.529357

Filename

529357